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Open LLM Leaderboard

Made by open-llm-leaderboardKitResearch
Newversion 1 · updated
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Overview

Ranks and compares open large language models in a React page with sorting, filtering, search and pinning. For people choosing or evaluating open models and chatbots.

Open LLM Leaderboard is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • A web browser, which is all the online page needs
  • Docker and Python with pip, for a local copy
  • The settings listed in .env.example
Runs in
  • A web browser, on the Space's page on Hugging Face
  • Windows, macOS or Linux with Docker, from a local build of the Space's files
Start with
pip install -U huggingface_hub
hf download open-llm-leaderboard/open_llm_leaderboard --repo-type space --local-dir open_llm_leaderboard
cd open_llm_leaderboard

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system
DockerContainer

About

What it is

Open LLM Leaderboard is a web app for tracking, ranking and comparing open large language models and chatbots in an open, reproducible way. It has a React front end and a FastAPI back end, and it runs as a Docker app.

Who it suits

It suits researchers, model builders and developers who need to compare open models side by side before they pick one.

IndustryWho uses itHow they use itExample
AI researchResearchersCompare open models in a reproducible waySort the table and pin a few models
Model developmentModel buildersSee how their model ranks against othersSearch for a model by meaning
SoftwareDevelopersPick an open model for a projectFilter the table, then compare pinned models

If you want to run a model yourself, you can skip it, because the page ranks models and does not host them.

Where to use it

  • A web browser, on the Space's page on Hugging Face
  • Windows, macOS or Linux with Docker, from a local build of the Space's files
Use caseWhoWhat they get back
Rank open modelsResearchersAn interactive table with sorting and filtering
Find a modelDevelopersResults from a semantic model search
Compare a shortlistAnyonePinned models side by side

How to set it up

  • A web browser, which is all the online page needs
  • Docker and Python with pip, for a local copy
  • The settings listed in .env.example
  1. Open the Space's page on Hugging Face to use it online. Nothing needs installing.
  2. For a local copy, install the Hugging Face hub package and download the Space's files.
  3. Move into the downloaded folder.
  4. Build the Docker image, then run it with port 7860 published.
pip install -U huggingface_hub
hf download open-llm-leaderboard/open_llm_leaderboard --repo-type space --local-dir open_llm_leaderboard
cd open_llm_leaderboard
docker build -t open_llm_leaderboard .
docker run -it -p 7860:7860 open_llm_leaderboard

The README also gives a Compose route for development.

docker-compose up

How to use it

Open the page and use the table. It is a modern React interface, with dark and light modes and a layout that adapts to small screens.

ToolWhat it does
Interactive tableSorts and filters the leaderboard
Semantic searchFinds models by meaning and not by exact name
PinKeeps chosen models in view for comparison
VirtualizationKeeps a long table fast to scroll

Example

Sort the table by a column you care about, pin three models that look interesting, and compare them side by side while you decide which to try.

For newcomers and experts

Open the Space page and sort the table first. Then pin two or three models, so you can read them next to each other.

The front end uses React, Material-UI, TanStack Table with TanStack Virtual, and an Express server for production. The back end is FastAPI, with services for the leaderboard, models, votes and the Hugging Face API.

  1. The React page asks the back end for data
  2. FastAPI services build the leaderboard
  3. The Hugging Face API supplies model data
  4. The page renders a virtualized table

Good to know

The online copy runs on upgraded CPU hardware, and a local copy needs the settings from .env.example.

  • License: Apache 2.0
  • Cost to run: the settings in .env.example, and Docker for a local copy
  • Data it reaches: model data from the Hugging Face API

README and files

README

Open LLM Leaderboard

Ranks and compares open large language models in a React page with sorting, filtering, search and pinning. For people choosing or evaluating open models and chatbots.

Made by open-llm-leaderboard on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: Open LLM Leaderboard is a web app for tracking, ranking and comparing open large language models and chatbots in an open, reproducible way. It has a React front end and a FastAPI back end, and it runs as a Docker app.

Who it suits: It suits researchers, model builders and developers who need to compare open models side by side before they pick one.

Industry | Who uses it | How they use it | Example
AI research | Researchers | Compare open models in a reproducible way | Sort the table and pin a few models
Model development | Model builders | See how their model ranks against others | Search for a model by meaning
Software | Developers | Pick an open model for a project | Filter the table, then compare pinned models

If you want to run a model yourself, you can skip it, because the page ranks models and does not host them.

Where to use it:

  • A web browser, on the Space's page on Hugging Face
  • Windows, macOS or Linux with Docker, from a local build of the Space's files
Use case | Who | What they get back
Rank open models | Researchers | An interactive table with sorting and filtering
Find a model | Developers | Results from a semantic model search
Compare a shortlist | Anyone | Pinned models side by side

How to set it up:

  • A web browser, which is all the online page needs
  • Docker and Python with pip, for a local copy
  • The settings listed in .env.example
  1. Open the Space's page on Hugging Face to use it online. Nothing needs installing.
  2. For a local copy, install the Hugging Face hub package and download the Space's files.
  3. Move into the downloaded folder.
  4. Build the Docker image, then run it with port 7860 published.
pip install -U huggingface_hub
hf download open-llm-leaderboard/open_llm_leaderboard --repo-type space --local-dir open_llm_leaderboard
cd open_llm_leaderboard
docker build -t open_llm_leaderboard .
docker run -it -p 7860:7860 open_llm_leaderboard

The README also gives a Compose route for development.

docker-compose up

How to use it: Open the page and use the table. It is a modern React interface, with dark and light modes and a layout that adapts to small screens.

Tool | What it does
Interactive table | Sorts and filters the leaderboard
Semantic search | Finds models by meaning and not by exact name
Pin | Keeps chosen models in view for comparison
Virtualization | Keeps a long table fast to scroll

Example: Sort the table by a column you care about, pin three models that look interesting, and compare them side by side while you decide which to try.

For newcomers and experts: Open the Space page and sort the table first. Then pin two or three models, so you can read them next to each other.

The front end uses React, Material-UI, TanStack Table with TanStack Virtual, and an Express server for production. The back end is FastAPI, with services for the leaderboard, models, votes and the Hugging Face API.

The React page asks the back end for data
FastAPI services build the leaderboard
The Hugging Face API supplies model data
The page renders a virtualized table

Good to know: The online copy runs on upgraded CPU hardware, and a local copy needs the settings from .env.example.

  • License: Apache 2.0
  • Cost to run: the settings in .env.example, and Docker for a local copy
  • Data it reaches: model data from the Hugging Face API

On Hugging Face, 13 November 2026

  • Likes: 14,156
  • Built with: docker
  • License: apache-2.0

Links

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.

Files
README.md  4 KB

1 file, 4 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

Compare with similar

Listed Hugging Face Spaces in the same category that share the most tags with this one.

ProductLikesBuilt withLicenseLast change
Open LLM Leaderboard This page14,156dockerapache-2.0
MTEB LeaderboardShares docker · leaderboard7,773dockermit
UGI LeaderboardShares docker · leaderboard2,199dockerapache-2.0
CADGenBench LeaderboardShares docker · leaderboard90dockerapache-2.0
Microduck SandboxShares docker804dockerapache-2.0
Visualize Dataset (v2.0+ latest dataset format)Shares docker754dockerapache-2.0
Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

Reviews

No ratings yet

Write a review

No reviews yet.

What people say

Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search and on Trendshift. Mindket doesn’t write or check them. Open one to read it where it was posted.

dnhkngShow HN: How I topped the HuggingFace open LLM leaderboard on two gaming GPUsI found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants. The weird finding: single-layer duplication…495 points · 126 repliesbelterOpen LLM Leaderboard11 pointsanotherpaulgOpen LLM Leaderboard – A Hugging Face Space by HuggingFaceH410 pointstoshOpen LLM Leaderboard6 pointsskilledOpen LLM Leaderboard v24 points · 1 replysshrootOpen LLM Leaderboard 22 pointspalmfacehnOpen LLM Leaderboard1 pointdavidbarkerOpen LLM Leaderboard 21 point

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